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Article

Achieving Supply Chain Sustainability Through Green Innovation: A Dynamic Capabilities-Based Approach in the Logistics Sector

by
Ahmad Ali Atieh
1 and
Mastoor M. Abushaega
2,*
1
Faculty of Business, Middle East University, Amman 11831, Jordan
2
Department of Industrial Engineering, College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(13), 5716; https://doi.org/10.3390/su17135716 (registering DOI)
Submission received: 17 May 2025 / Revised: 12 June 2025 / Accepted: 17 June 2025 / Published: 21 June 2025
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Abstract

:
This study examines the effect of internal dynamic capabilities i.e., digital leadership, environmental awareness, and organizational learning, on sustainable supply chain performance as studied in the logistics sector. It builds on the Dynamic Capabilities Theory by combining notions of green innovation and sustainability and fills the growing gap in the existing literature. Despite the fact that these domains have been extensively studied independently, there has been limited research examining how internal capabilities contribute to green supply chain innovation (GSCI) that in turn results in sustainability outcomes, especially in the case of emerging markets. Seven hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis of data collected from 312 logistics and supply chain professionals in Jordan. This study shows that each of the three capabilities has a major effect on GSCI and therefore sustainable performance. Linking the most influential predictor of sustainability outcome to sustainable supply chain performance, as indicated by the strongest effect (β = 0.825, p < 0.001) between GSCI and sustainable supply chain performance, and followed by significant coefficients between the sustainable information processing (β = 0.261, p < 0.01), and information capabilities (β = 0.297, p < 0.001), indicates that the theory is more suited to GSCI. In particular, digital leadership had the largest impact on the green innovation (β = 0.481, p < 0.001), indicating that the role of digitally driven leadership is to facilitate eco-innovation. In addition, this intermediate factor, GSCI, serves as a variable that mediates relationships between the capabilities and the sustainability outcomes. As the results here suggest, leveraging internal capabilities is a very tangible channel for green innovation that has important ramifications for practitioners and policymakers facing resource constraints.

1. Introduction

With the state of the environment continuing to deteriorate, the supply chain continuing to be disrupted, and the amount of stakeholder pressure increasing, sustainability has become a critical strategic objective across industries. The process of the related activities of the logistics sector in terms of supplying goods from source to the market has faced serious challenges, and ecological and operational challenges have made this transformation even more important [1]. In order to attain sustainable supply chain performance, environmentally conscious practices need to be adopted, but sustainable capabilities also need to be brought into play that support sustainable development pathways through continuous learning, adaptation, and innovation [2]. Thus, the conjunction of organizational behavior, environmental responsibility, and technological leadership is an appropriate nexus for current supply chain research. These challenges include high fuel dependency, fragmented infrastructure, limited adoption of green technologies, and regulatory gaps that slow down the transition toward sustainable logistics operations [3].
Green Supply Chain Innovation (GSCI) is one of the most promising approaches to achieve sustainability by integrating environmental concerns into several operations of the supply chain through innovative products, processes, and relationships [4,5]. GSCI is also recognized as a crucial lever for minimizing the environmental footprint, as well as for increasing performance and competitive edge. But such innovation cannot simply be achieved by operational adjustments; it requires capabilities of an organization that are forward looking, that are dynamic, and that are strategically aligned with sustainability objectives [6]. In addition, it requires dynamic capabilities that enable sensing, seizing, and transforming those processes that are central tenets of the Dynamic Capabilities framework [7].
This study contributes theoretically by integrating three core capabilities within the Dynamic Capabilities lens that have not been jointly studied in the context of sustainable logistics. Practically, Jordan presents an ideal context due to its strategic geographical location, active economic and environmental reforms, and the logistics sector’s increasing role in national development [8].
Furthermore, this study finds its roots in the Dynamic Capabilities Theory (DCT), which argues that in order to achieve a competitive advantage in a rapidly changing environment, an organization should develop some ability to sense environmental changes, seize emerging opportunities, and modify its internal competencies [7]. Deploying DCT within the context of sustainable supply chains helps to comprehend what firms can do to make use of its internal drivers, such as leadership, awareness, and learning, in supporting the requisite innovation and long-term sustainability.
However, this study concentrates on three of the internal enablers that are related to dynamic capabilities: digital leadership, environmental awareness, and organizational learning. Digital leadership is a firm’s power to determine digital transformation with a sustainability vision [9]. Environmental awareness is organization’s awareness of ecological risks and the responsibility to act accordingly [1]. At the same time, organizational learning refers to the firm’s ability to acquire, disseminate, and use knowledge that predisposes the firm to be capable of uninterrupted improvement and innovation [2,10]. All these factors are hypothesized to affect the firm’s ability to undertake green supply chain innovation that will lead to enhanced sustainable performance [11,12].
These capabilities have been growing in importance, and the academic literature has generally been silent on joint empirical investigation of these capabilities; how they together drive GSCI; and GSCI’s contributions to sustainability outcomes, particularly in emerging economies where logistics systems are confronted with structural, technological, and environmental constraints. However, this study delves into that gap by studying the case of the logistics sector in Jordan, an emerging market that is actively implementing environmental and economic reforms. Using the mediating role of green innovation, the research seeks to understand whether and how internal organizational resources (capabilities) can be leveraged to achieve a more sustainable outcome when conducting business in such complex supply chain environments. Despite their growing importance, existing research has yet to explore how these dynamic capabilities jointly interact to drive green innovation in logistics. While some studies address individual factors, there is a lack of integrated models linking digital leadership, environmental awareness, and organizational learning to sustainability outcomes, especially in emerging markets where such capabilities are still evolving [13].
To address these questions, we employed a quantitative research design, collecting data via structured questionnaires from 312 professionals in the logistics sector in Jordan. The data were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to test the hypothesized relationships and the mediating role of green supply chain innovation [14]. By combining digital leadership, organizational learning, and environmental awareness into one unified model, this study contributes new insights into how these three core dynamic capabilities may be combined. It also shows that green supply chain innovation serves a mediating function, which provides new directions of theory and practice in sustainable logistics.
Accordingly, this study seeks to answer the following research questions:
  • RQ1: How do digital leadership, environmental awareness, and organizational learning influence green supply chain innovation in the logistics sector?
  • RQ2: How does green supply chain innovation mediate the relationship between internal dynamic capabilities and sustainable supply chain performance?
Our study explores Dynamic Capabilities Theory in supply chain sustainability to offer value to firms who want to combine innovation and sustainability in their supply chain methods.

2. Literature Review and Hypotheses Development

2.1. Digital Leadership

Digital leadership makes sure an organization can steer digital change programs when they match its core plans and boost product development efforts against external obstacles [15]. Digital leaders need both strong technical skills and the power to develop a flexible organization focused on environmentally conscious progress. In supply chain management, digital leadership helps companies use intelligent systems and make data decisions while creating sustainable and efficient solutions [16,17]. Studies show digital leadership helps organizations find new digital trends and use them to build better business positions [18]. Digital leadership serves as the fundamental ability that leads supply chains toward a more sustainable and innovative future.
Digital leadership defines how fast and well organizations transform digitally through their efforts to spark environmentally friendly innovation. Companies achieve their climate targets and install smart systems faster when their digital leaders have both vision and technology skills [19]. A leader with this mindset helps teams try new ideas at a rapid pace so they can succeed as innovators in green supply chains [20]. Data show that leaders with strong digital orientation plus commitment to environmental practices help their teams develop eco-innovation initiatives [20].
H1. 
Digital leadership has a significant positive effect on green supply chain innovation.

2.2. Environmental Awareness

Organizations demonstrate environmental awareness through their ability to identify and react to ecological threats linked to their business activities and stakeholder community. Dynamic companies with strong environmental knowledge adopt eco-friendly methods and environmental rules plus launch green development programs [21]. Organizations make supply chain choices about resource saving and emission cuts when they prioritize environmental understanding [22,23]. According to [24], firms need to develop environmental awareness first so they can detect external demands and modify their business plans sufficiently. Business revenues and operations rely on it to begin green supply chain practices while making sustainability core to all operational activities.
The concept of environmental awareness refers to the level of responsiveness and recognition of an organization to determine ecological threats and the demands of sustainability. Proactive strategies for environmental impact reduction, compliance with the regulations, and development of corporate responsibility practices are associated with high environmental awareness [25]. This awareness is a stimulus for green innovation: it motivates firms to redesign products and processes with an understanding of sustainability [26]. In the same vein, the likelihood of organizations investing in innovative green supply chain initiatives is high in cases where they have a high consciousness of the environment.
H2. 
Environmental awareness has a significant positive effect on green supply chain innovation.

2.3. Organizational Learning

Organizational learning refers to the ability of organizations to gain knowledge that they pass along and put to use in order to make present systems better while also developing new approaches [27,28]. Organizations need this learning to become more flexible and stronger when handling various environmental challenges like supply chain breakdowns and changing sustainability requirements. Learning organizations use better methods to identify new technology and provide effective sustainable solutions before applying them at work for enhanced results [29]. Organizational learning can build dynamic capabilities that then advance sustainable supply chain improvement according to [30,31].
Organizational learning allows firms to continually acquire and relate to knowledge to enhance processes and adapt to change. In terms of recognizing emerging sustainability trends, exploring ecofriendly technologies, and institutionalizing innovative practices, learning oriented organizations are able to achieve this more effectively [25,32]. Learning as a driver of GS is relevant in the context of green supply chain management as it supports the development of GS capabilities needed for implementation of new environmental solutions and for reacting appropriately to regulatory and market pressures [33].
H3. 
Organizational learning has a significant positive effect on green supply chain innovation.

2.4. Green Supply Chain Innovation

Green Supply Chain Innovation (GSCI) refers to the combination of new practices and technologies, as well as collaborative approaches that integrate environmental sustainability into supply chain operations. Such innovations can be ecofriendly product designs, green manufacturing, cleaner logistics, reverse logistics, and various carbon footprint reduction strategies [34,35]. GSCI is becoming an instrument for competitive advantage and the solution to ecological issues [5]. According to empirical evidence, firms acting under GSCI tend to perform better when it comes to compliance and efficiency as well as in satisfying their stakeholders [33]. Furthermore, GSCI serves as a mediating variable to link organizational capabilities (like leadership and learning) and performance outcomes into sustainable practices.
While studies such as Junaid [36] and Moh’d [32] have examined the positive outcomes of GSCI, the literature still lacks comprehensive models that connect dynamic internal capabilities with GSCI and long-term sustainability performance, particularly in logistics sectors in emerging economies.
Furthermore, GSCI encompasses the development and implementation of environmentally responsible processes, technologies, and strategies across the supply chain. Research consistently shows how GSCI creates better environmental performances while saving costs and makes businesses compliant with regulations along with pleasing stakeholders [37,38]. With better resource management, GSCI makes the supply chain more sustainable by helping the environment and saving resources while also creating better business value.
H4. 
Green supply chain innovation has a significant positive effect on sustainable supply chain performance.
Though essential skills like digital leadership and organizational learning from the base of internal capabilities, their straight connection with sustainability achievement remains unclear. These capabilities help the firm develop new environmental supply chain practices that result in better sustainability outcomes. Green supply chain innovation serves as a linking role between dynamic capabilities and their sustainable effects, as described by [7,39].
H5. 
Green supply chain innovation mediates the relationship between digital leadership and sustainable supply chain performance.
H6. 
Green supply chain innovation mediates the relationship between environmental awareness and sustainable supply chain performance.
H7. 
Green supply chain innovation mediates the relationship between organizational learning and sustainable supply chain performance.

2.5. Sustainable Supply Chain Performance

Sustainable Supply Chain Performance describes how well a supply chain meets its environmental, economic, and social targets simultaneously. Instead of using basic cost and efficiency measurements, it adds environmental goals like carbon cutting plus social equality and future readiness standards [40]. Companies that make sustainability their supply chain priority will gain better brand standing plus save money while avoiding problems [41,42]. A successful SSCP implementation depends on various technical updates plus company partnerships alongside followed-up business goals that benefit from dynamic business methods together with sustainable ideas.
Innovation-driven capabilities have an increasing impact on sustainable performance under the pressure to meet increasing environmental regulations and stakeholder demands [43]. While there is a growing body of research, a unified framework is still lacking that empirically links internal dynamic capabilities to sustainability through green innovation in the context of emerging economies. This gap is addressed by our study through testing an integrated model in the Jordanian logistics sector.

2.6. Contextualisation Within Jordan and the MENA Region

Sustainability challenges in the logistics sector become more acute in the context of Jordan and the larger MENA region due to limited infrastructure, resource constraints, and regulatory fragmentation. Global studies, especially those from Europe and Southeast Asia, believe in integrated technology and developed regulatory systems, whereas the Middle East still suffers from inconsistent environmental policies, lower levels of green innovation adoption, and slow digital transformation [44,45]. Nevertheless, a plethora of developments are being carried out by Jordan today, spurred by such recent initiatives as Jordan’s Vision 2025 and working partnerships with international development organizations to modernize logistics and the transport system. These new reforms offer a rare chance to explore the degree to which internal organizational capabilities are mobilized to surmount challenges related to the structure and ecology of the environment in order to advance sustainable supply chain performance in such settings. Specifically, this study is rooted in these local dynamics and takes a global viewpoint, complementing both at the same time.

2.7. Theoretical Background

The Dynamic Capabilities Theory developed by Teece, Pisano, and Shuen in 1997 [7] helps companies study their ability to build essential skills and control internal assets for successful handling of changing market conditions. Unlike RBV, which studies how firms can succeed through static resources, DCT examines how organizations prioritize market trends and modify their operations for better market success in times of change. This theory provides supply chain leaders with a strong framework to create future success and sustainability by studying their company’s changing needs.
The three major types of dynamic capabilities include sensing, seizing, and transforming. Companies that can read the market and external conditions effectively are known as sensors of useful market data. Organizations use available resources to secure exciting business possibilities while putting money into new projects. Organizations transform themselves by maintaining skill enhancement and operating system adjustments to follow strategic plan changes [46]. The three measurement factors match the research goals to study digital leadership, environmental awareness, and organizational learning, which helps build dynamic capabilities for an organization.
Digital leadership, environmental awareness, and organizational learning are chosen as they relate to the three core dimensions of Dynamic Capabilities Theory (sensing, seizing, and transformation). Digital leadership of an organization means its ability to perceive the technological tendencies and to engage digital changes with sustainability goals. Firms can sense and seize opportunities for proactive compliance and innovation as a result of an environmental awareness that makes possible the detection of ecological risks. Second, organizational learning underpins the capacity for reflection in order to transform the internal learning routines, engrave the new learning, and also continuously innovate in a turbulent environment (transforming). Together, these three capabilities are internal mechanisms that enable the creation of adaptive and innovative supply chain practices, and this is consistent with how the dynamic capabilities framework [7] is defined.
Digital leadership shows how well a company leads its digital transformation process while matching it to its main strategic and sustainable objectives [47]. A digital leader who sees potential in new technologies helps the business strive for innovation while staying adaptable through dynamic capability development. Organizations need environment awareness to understand threats and laws before starting their green tech approaches [48]. A company’s environmental consciousness helps it predict future ecological issues and deploy ecologically safe measures before such problems happen. Organizational learning helps firms adopt new knowledge that they can distribute and integrate into improved business methods. Organizations that focus on learning can better modify their supply chain operations to include environmental factors [49,50].
Organizations use their dynamic capabilities to implement Green Supply Chain Innovation (GSCI). The organization uses dynamic capabilities to design green products and systems that protect the environment and maintain its marketplace leadership [33,43]. GSCI functions as the system that connects internal competencies to long-term supply chain results in all environmental economic and social aspects of logistics operations.
The study positions digital leadership, organizational learning, and environmental awareness as GSCI drivers while making GSCI impact sustainability outcomes through DCT. The theory strengthens both dynamic capability and green innovation research while delivering specific business guidance to organizations in resource-limited and unstable environments.

2.8. Conceptual Framework

This study adopts Dynamic Capabilities Theory [7] to find out which internal organizational strengths help supply chains become more sustainable. Organizations can use digital leadership, teamwork, and knowledge updates to support their green supply chain innovation efforts.
The three internal capabilities function together to create GSCI, which improves how suppliers manage sustainability. Internal abilities do not lead to sustainability results unless they trigger new ways of doing business.
This study framework contains seven hypotheses to analyze how three factors affect GSCI and how GSCI links with more sustainable performance. The model shows logistics firms in emerging markets how to use dynamic capabilities to create green innovation and achieve better environmental results.
The framework shows that digital leadership and environmental awareness produce sustainable supply chain performance through learning and green supply chain innovation. Green supply chain innovation connects all parts of this model together as it acts as an intermediary. Our study design, shown in Figure 1, displays the study connections.

3. Methodology

The study uses a quantitative approach to determine relationships between digital leadership, environmental awareness, organizational learning, green supply chain innovation, and sustainable supply chain performance in the logistics industry. The theoretical lens used to understand why internal capabilities support innovation and sustainability is the Dynamic Capabilities Theory, on which the research is grounded.
A structured questionnaire was developed based on previously validated scales from the literature. All items were measured using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Each of the five constructs in the study was represented by multiple measurement items (See Appendix A):
  • Digital Leadership: Measured using four items adapted from [51], focusing on the organization’s ability to lead digital transformation toward sustainable objectives.
  • Environmental Awareness: Measured using four items from [52,53], emphasizing the firm’s recognition of ecological risks and its proactive environmental actions.
  • Organizational Learning: Adapted from [54,55], including items related to acquiring, sharing, and applying new knowledge in response to sustainability challenges.
  • Green Supply Chain Innovation (GSCI): Assessed with five items adapted from [33,35], covering eco-friendly product design, cleaner production processes, reverse logistics, and carbon reduction strategies.
  • Sustainable Supply Chain Performance (SSCP): Evaluated using five items adapted from [26,56], addressing environmental, economic, and social performance dimensions of supply chains.
The target population of the study is managers, supervisors, and operational staff working in logistics and supply chain related jobs in distribution companies and third-party logistics providers in Jordan. The sampling technique was purposed to ensure the respondents have experiences in digital transformation, sustainability, or supply chain operations.
The questionnaires were physically and electronically distributed to 400 people for a total of 3 months. Upon removing incomplete and invalid responses, 312 valid responses were used for analysis, yielding a response rate of 78%, which is acceptable for SEM based research [57]. The participating organizations were diverse, encompassing various sizes, ownerships (local versus international), and years of operation.

3.1. Data Analysis Method

Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4 software, the data were analyzed. SmartPLS 4 was configured to use the path weighting scheme with a maximum of 300 iterations and a stop criterion of 10−7. Bootstrapping was conducted with 5000 subsamples to test the significance of the model paths using bias-corrected confidence intervals. As PLS-SEM is appropriate for exploratory studies, is robust with complex models with mediating effects, and is capable of using smaller sample sizes and non-normal data distributions [58], it was chosen as the appropriate method to use. It included two main stages that proceed sequentially: measurement model evaluation (validity and reliability) and structural model evaluation (hypothesis testing and path analysis) [59].
To assess construct reliability and validity, the following tests were conducted:
  • Cronbach’s Alpha and Composite Reliability (CR) for internal consistency (acceptable if >0.70)
  • Average Variance Extracted (AVE) for convergent validity (>0.50)
  • Fornell–Larcker criterion and HTMT ratios for discriminant validity (<0.90).
Additionally, VIF values were checked to ensure the absence of multicollinearity.

3.2. Sample Profile

The final sample consisted of 312 respondents occupying management and supply chain positions in different firms in Jordan. The sample had a diverse mix of functional roles, supply chain managers (32%), logistics supervisors (25%), procurement officers (18%), and operations specialists (25%).
Regarding organizational size, 44% of the respondents reported working in medium sized enterprises (50–249 employees), 33% in large enterprises (250+ employees), and 23% in small enterprises (less than 50 employees). With regards to ownership structure, 61% and 39% of the firms were locally and internationally or joint venture owned, respectively.
Of the respondents, 68% had more than 5 years of experience in logistic-related work; thus, they are very familiar with digitalization and sustainability practices. Additionally, 57 percent of the firms already implement green technologies or initiatives in the context of supply chain operations.
A purposive sampling technique was used to determine the number of respondents. Specifically, the study aimed at persons who are working in jobs in logistics and supply chain management in different firms in Jordan. The first attempt to make contact was through the membership directories of professional associations such as Jordan Logistics Association and Jordan Chamber of Commerce. The survey was circulated via email and LinkedIn to respondents who needed to have at least three years’ experience at some point in the past in logistics and/or supply chain and worked directly on digital and/or sustainability initiatives.
The study included participants from differently sized companies (small, medium, and large enterprises), different ownership structures (local, international, and joint ventures), and from different functional departments, with the purpose to make the survey representative and to reduce the selection bias. The process of checking the level of representation of these categories is extremely important because the response process was monitored to obtain balanced representation. After screening out and cleaning the incomplete or inconsistent submissions, the final sample size was 312 valid responses that satisfied the recommended minimum sample size for the PLS-SEM analysis with robust statistical power [14].
Therefore, this profile indicates that the sample is, by all indications, fit to offer direction concerning green innovation and sustainability performance dynamics in the logistics sector of Jordan.

3.3. Instrument Design

The questionnaire was designed on the basis of validated scales, available in previous studies of digital leadership, environmental consciousness, organizational learning, green supply chain innovation, and sustainable supply chain performance. All the constructs were measured by reflective multi-item scales answered using a five-point Likert scale, where 1 stands for strongly disagree and 5 for strongly agree.
Several procedural and statistical controls were employed to minimize subjectivity and common method bias. The questionnaire was anonymized, and the respondents were assured that there are no right or wrong answers in order to reduce social desirability bias. To detect inattentive responding and reduce acquiescence bias statistically, we included reverse coded control items in each section. Furthermore, Harman’s single-factor test and the Variance Inflation Factor (VIF) test were performed to measure common method variance, and it has been shown to be within tolerable levels.
Thus, the questionnaire was piloted using 20 logistics professionals to ensure clarity and to subsequently make minor modifications. Cronbach’s Alpha, Composite reliability (CR), and Average Variance Extracted (AVE) were used for the assessment of items reliability and validity. The internal consistency and convergent validity of the instrument was confirmed because all values were larger than the recommended thresholds (α > 0.7, CR > 0.7, AVE > 0.5).

4. Measurement Model Evaluation

We used PLS-SEM to analyze measurement model reliability and validity through a set of tests run on SmartPLS 4. The examination tested how well each criterion determined reliability in individual measures along with reliability within individual measures.
Furthermore, we checked indicator reliability through the analysis of each item’s factor loadings. Ref. [58] state that loading values greater than 0.70 are satisfactory for acceptance. Most study items reached this baseline level, which shows that each element of these groups makes a significant addition. Several items were kept for analysis because they related well to theory and supported the main concept.
However, to determine internal consistency, we tested both Cronbach’s Alpha and Composite Reliability values. All measured constructs showed Cronbach’s Alpha and Composite Reliability figures higher than 0.70 to confirm their reliability, as identified in the study by [57]. The items within each construct show clear evidence of measuring the same basic principle.
Additional validity assessment depended on the Average Variance Extracted (AVE). A research study by [60] showed that AVE values should reach 0.50 or above because 50% of the measurement data need proper explanation. The test results show that all constructs comply with convergent validity standards because their AVE values exceed 0.50.
We then evaluated discriminant validity through both Fornell–Larcker criterion and HTMT tests. For Fornell–Larcker analysis, the square root of AVE must exceed all correlations between variables. The necessary criteria were applied consistently in all investigated cases. The results indicated discriminator validity since HTMT scores stayed below 0.85, following the strict model proposed by [61]. The testing of our structural model and research ideas requires measurement model constructs with this level of reliability and validity.
Table 1 shows the results of our measurement model evaluation. All factors demonstrate good connection since their loadings surpass 0.70. The measurement model confirmed high internal reliability because CR and Cronbach’s Alpha values for each construct surpassed 0.70. The values for each measurement model were above 0.50 AVE, which proved convergent validity as stated by [60,62]. The research findings support the quality of the measurement approach.
Figure 2 shows the standardized factor loadings for each construct as the measure model results. The reliability of all indicators is satisfactory, and they make a significant contribution to their respective latent variables.
Table 2 shows the HTMT values for all construct pairs. Since all values fall below the conservative threshold of 0.85, discriminant validity is determined [61].
Table 3 shows the results of Fornell–Larcker criterion. The diagonal values are of the square root of the AVE and exceed the Interco struct correlations, indicating discriminant validity [60].

5. Structural Model Evaluation

Following validation of the measurement model, the structural model was evaluated for testing the hypothesized relationship with the help of a sequential approach using PLS-SEM (partial least square structural equation modeling) with SmartPLS version 4. A bootstrapping procedure with 5000 subsamples was executed to estimate the significance of the path coefficients.
The results demonstrate that digital leadership, environmental awareness, and organizational learning each have a significant positive effect on green supply chain innovation, thereby proving their role as dynamic capabilities. Consequently, sustainable supply chain performance (SCP) was found to have a very strong and positive statistically significant effect on green supply chain innovation.
The R2 values showed good explanatory power of the model in explaining green supply chain innovation and sustainable supply chain performance, with their means being 0.672 and 0.621, respectively. The values in these results indicate that a good deal of variance in both constructs was explained by the independent variables.
Moreover, the value of the SRMR was 0.061, which was good for the model fit. Finally, the results overall validate the soundness of the model structurally and theoretically in support of the framework based on the Dynamic Capabilities Theory.
Table 4 shows the estimates of the structural model for the path analysis of the present study. All hypothesized relationships were tested and were supported at a significant level of p < 0.05, thus indicating that there is a positive relationship between digital leadership, environmental awareness, and organizational learning and green supply chain innovation as well as sustainable supply chain performance.
Figure 3 presents the results of structural model estimate and standardized path coefficients that indicate the significance of the causal relationships examined in the study. All paths were also found to be significant at p < 0.05 level, which attests to the fact that digital leadership, environmental awareness, and organizational learning have positive impacts on green supply chain innovation, which in turn improves sustainable supply chain performance.
As shown in Table 5, the R2 values of the endogenous constructs are as follows. The overall analysis of the hypotheses shows that they explain 61.1% of the green supply chain innovation and 68.0% of the sustainable supply chain performance. These values indicate a significant amount of explanatory value. To evaluate the model stability and robustness, Variance Inflation Factor (VIF) values were calculated for all predictor constructs in structural models. The results obtained show that the VIF values of all the latent variables were far below the threshold recommended (5), hence no multicollinearity problems among the constructs existed. This guarantees the correctness of the path coefficient estimations and economy of times for reconstructing the validity of the model structure.

Mediation Analysis

To examine the indirect effects of DL, EA, and OL on effective SCP through GSCI, the mediating effect analysis was conducted using the bootstrapping technique in SmartPLS with 5000 resamples.
This implies that GSCI fully mediated all three relationships under study. In the present study, the above analysis supported the idea that GSCI mediated the relationship between digital leadership and sustainable supply chain performance as a partial mediator, as the indirect effect was both positive and statistically significant (z = 8.156, p < 0.001). As for environmental awareness, although we did not find any direct effect with a significant coefficient (β = 0.077, n.s.), it proved to have an indirect impact through GSCI (β = 0.124, p < 0.01), which means that this construct intervention effect is partly due to innovation. Learning also had an indirect effect on performance through GSCI controlling for the effect of innovation on the GSCI (t = 3.232, CI [3.026, 3.484], p < 0.001), thus supporting the hypothesized path between learning and sustainability outcomes.
The findings in the present study also offer empirical evidence to the proposed conceptual model and testify to green supply chain innovation as a mediator between dynamic capabilities and sustainability performance. The findings make sense in the context of the dynamic capability’s theory, which asserts that innovation entails the exercise of internal capabilities to create sustainable value propositions for the organization [7].

6. Discussion and Implications

This study supports, from the context of internal dynamic capabilities, necessary conclusions for the impact and influence of digital leadership, environmental awareness, and organizational learning with GSCI and SSCP in the logistics sector.
From a practical point of view, the result also shows that green supply chain innovation (GSCI) can be successfully integrated into logistics operations by means of some techniques, such as cleaner transportation modes, eco-friendly packaging, reverse logistics systems, and digital carbon monitoring. In the case of logistics firms, practices like this should be applied in their operational workflows if they are to realize both environmental and cost-related efficiencies.
First, the findings show that digital leadership exerts an influence with the desired signs on GSCI and SSCP. This goes with the notion that digitally oriented leaders have a principal role to undertake in directing organizations to innovation and sustainability by championing digitization and extending environmental sustainability strategies [63,64]. Both the primary and secondary impacts of digital leadership support its necessity for developing a positive cultural change and achieving organizational goals.
Organizations are advised to invest in executive development programs that involve sustainability goals, appraise internal digital transformation champions, and advance participatory leadership that encourages innovation across departments to develop strong digital leadership.
Second, the results revealed that environmental awareness is an important antecedent of green innovation and sustainability performance. This partially supports the notion that awareness denotes proactivity through current and future regulation and stakeholders’ responsiveness, as affirmed in the preceding literature by scholars such as [24,65]. The result also reveals that awareness is not only a compliance issue but also activates stimulating, fresh, and sustainable thinking for major value generation. Training programs, internal sustainability reporting systems, and stakeholder engagement strategies should be implemented by firms to facilitate environmental awareness.
Third, organizational learning was proven to be statistically significant to GSCI and SSCP. This is adequately supported by the Dynamic Capabilities Theory that refers to the competency of an organization in changing, creating new value, and upgrading learning processes [46,66]. Learning is the process through which firms can undertake a hunt for new technologies, redesign processes, and embed sustainable practices.
Moreover, the study establishes that green supply chain innovation acts as a moderator between sustainable performance and all three capabilities. This fact only proves that innovation is the channel through which internal competencies are translated into functional, environmental, and operational results [32,35,67].
From a policy perspective, we recommend that governments and regulatory authorities formulate tax incentives for logistics firms that apply certified green logistics technologies, spread subsidies and low interest loans for digital sustainability innovations, and set up public private partnerships with supply chain decarbonization programs. Such targeted interventions would make life for innovation-driven sustainability practices easier.
However, from a theoretical standpoint, the paper contributes to the Dynamic Capabilities Theory by illustrating how internal capabilities influence innovation to arrive at sustainability performance. Although the above-stated concepts have been explored by previous studies individually, this research synthesizes them into a single model that shows how sustainability is obtained through capability-driven innovation.
Accordingly, the findings can be considered from the perspective of providing a practical approach and useful recommendations concerning the priorities of managers employed in the sphere of logistics. Organizations need to incorporate strategies related to leadership development, learning, and awareness concerning the environment, and these strategies need to be implemented in the organization. More importantly, they should consider green innovation as more important since it can act as a roadmap to long-term innovations for sustainability. This transition should be backed by policies focusing on the development of digital infrastructure, promotion of green technologies, and education that strengthens organizational skills and capacities. This transition should be backed by investments in digital infrastructure, green technology development, and capacity-building programs tailored for the logistics sector in emerging economies.
Last, the study finds that supply chain sustainability is not a matter of external requirements or separate efforts, but it is a mutually linked internal management of leadership, consciousness, knowledge, and creativity. These perspectives are valuable mostly for the firms who are performing in emerging economies such as the Jordanian markets, where the pressure and intensity of change is keen but at the same time the possibility of change is enormous.

7. Conclusions and Recommendations

The purpose of this research was to understand how aspects like the leadership of digital tools, awareness of environmental issues, and workforce learning support sustainability in a supply chain, either directly or through innovations associated with “green” systems. DCT, an approach used to explain how organizations become better, was used. Based on this theory, effective leadership, quick responses to different situations, and fast learning make it easier for organizations to respond to changes, mainly when it comes to sustainability and innovation.
The use of PLS-SEM revealed that better digital leadership, understanding of the environment, and strong organizational learning positively influence green supply chain innovation. It was found that green supply chain innovation helps enhance the sustainable performance of supply chains. This confirms the theory that innovation, and particularly green innovation, helps internal skills transform into better environmental, social, and economic achievements for a company’s supply chain.
It adds to our understanding by putting forward a model that brings together internal strengths and their effect on sustainability, an aspect that was not well studied before, mainly in developing countries. Since the model covers several areas, it helps bridge a significant gap by proving how different factors can boost both innovation and sustainability. It also enables the use of Dynamic Capabilities Theory to solve current issues in supply chain management.
Based on these findings, some useful suggestions are proposed to organizations that wish to enhance their sustainable supply chain performance. We should start by making sure organizations encourage digital leadership growth using intended training and activities that concentrate on guiding digital change with sustainability in mind. Leaders should have both professional knowledge and a clear approach for boosting sustainability and growth over time. Next, every organization should make environmental awareness a part of their daily tasks and ways of working. This is possible by holding awareness programs, putting green policies in place, and rewarding those who are responsible for the environment.
It is also necessary to pay attention to developing organizational learning efforts. Sharing information among teams, setting up ways to review experiences, and collecting knowledge from environment-related work can help the company improve continually. Lastly, green innovation is encouraged by strengthening partnerships with all partners and stakeholders. Accelerated results in sustainability and competition could be achieved by jointly developing environmentally friendly products and packages as well as smarter logistic processes.
It is important for governments and regulators in developing countries to play a key role in policy. These programs should motivate companies to invest in technologies that favor green transformation. Moreover, teaming up public and private entities can help small and medium businesses use sustainable strategies and join green supply chain processes. Policies for digital transformation must aim for sustainability so both the economy and environment are developed together.
It would be helpful for researchers to study the changes in relationships between internal capabilities, innovation, and performance over time by using a longitudinal study. Examining factors that affect these situations in different regions or sectors can give a clearer picture of what is happening. Also, adding factors such as the role of authorities and updates in technology or workplace culture might improve understanding about sustainable development in supply chains.
There is also a need for policymakers and industry associations in developing countries such as Jordan to encourage green innovation and offer funding for training and the promotion of digital frameworks. In conclusion, companies should focus on implementing sustainable strategies and long-term vision of the further supply chain development based on the organization’s capabilities and innovations.

8. Limitations and Future Research Directions

This study offers useful information about how internal capabilities and green innovation improve sustainable supply chain performance, but it has specific problems. Research from this sector in Jordan might not apply to different sectors across various countries because their business conditions differ. Research should test this model with various industries and perform sustainability performance comparisons of developed and emerging markets.
The study uses a fixed-time survey method that stops us from knowing how performance evolves with time and makes it hard to prove connection between elements. Future research should track how organizations transform their abilities and select new choices while maintaining sustainability results.
The self-reported data collection method might lead to bias from survey participants since they reported their own responses. Future studies should add performance results from companies and combine interviews or business case studies to uncover the concrete details of using green innovations.
The study centered on three unique dynamic capabilities in its investigation. Future research should test how supporting relationships like top executives and supply chains affect the connection between green innovations and agility. Expanding this research model would reveal the complete set of elements that dictate successful sustainable supply chain performance.

Author Contributions

Methodology, M.M.A.; Data curation, M.M.A.; Writing—original draft, A.A.A.; Visualization, A.A.A.; Supervision, M.M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project number: JU-202503217-DGSSR-RP-2025.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Measurement Items

All items were measured on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).
1.
Digital Leadership
  • DL1: Our leadership has a clear vision for digital transformation aligned with sustainability.
  • DL2: Top management promotes digital technologies to support environmental goals.
  • DL3: We are proactive in adopting digital solutions for greener operations.
  • DL4: Our leaders encourage innovation in sustainable digital practices.
2.
Environmental Awareness
  • EA1: We understand the environmental risks associated with our supply chain activities.
  • EA2: The organization has policies that reflect environmental responsibility.
  • EA3: We actively monitor environmental compliance and regulations.
  • EA4: Employees are aware of the ecological impact of their activities.
3.
Organizational Learning
  • OL1: We learn from previous environmental mistakes to improve practices.
  • OL2: Knowledge about sustainability is shared across departments.
  • OL3: Our organization adapts quickly based on environmental feedback.
  • OL4: Employees are encouraged to propose eco-friendly improvements.
4.
Green Supply Chain Innovation (GSCI)
  • GSCI1: We design products that minimize environmental impact.
  • GSCI2: Our logistics processes reduce waste and pollution.
  • GSCI3: We implement reverse logistics systems.
  • GSCI4: We collaborate with suppliers for greener solutions.
  • GSCI5: We invest in low-emission technologies and materials.
5.
Sustainable Supply Chain Performance (SSCP)
  • SSCP1: Our supply chain reduces carbon emissions.
  • SSCP2: We achieve cost savings through sustainable practices.
  • SSCP3: Our operations meet environmental and social compliance.
  • SSCP4: Stakeholders view our supply chain as socially responsible.
  • SSCP5: Sustainability initiatives enhance our competitive position.

References

  1. Daily, B.F.; Bishop, J.W.; Govindarajulu, N. A Conceptual Model for Organizational Citizenship Behavior Directed toward the Environment. Bus. Soc. 2009, 48, 243–256. [Google Scholar] [CrossRef]
  2. Garvin, D.A. Building a Learning Organization. Bus. Credit. York 1994, 96, 19. [Google Scholar]
  3. Ruzieh, A.S. Advancing Middle East Construction Sustainability: A Framework for Addressing Logistics Challenges Through Solutions and Critical Success Factors. Sustainability 2025, 17, 533. [Google Scholar] [CrossRef]
  4. Feng, Y.; Lai, K.; Zhu, Q. Green Supply Chain Innovation: Emergence, Adoption, and Challenges. Int. J. Prod. Econ. 2022, 248, 108497. [Google Scholar] [CrossRef]
  5. Lai, K.; Feng, Y.; Zhu, Q. Digital Transformation for Green Supply Chain Innovation in Manufacturing Operations. Transp. Res. Part E Logist. Transp. Rev. 2023, 175, 103145. [Google Scholar] [CrossRef]
  6. Kronborg Jensen, J.; Balslev Munksgaard, K.; Stentoft Arlbjørn, J. Chasing Value Offerings through Green Supply Chain Innovation. Eur. Bus. Rev. 2013, 25, 124–146. [Google Scholar] [CrossRef]
  7. Teece, D.J.; Pisano, G.; Shuen, A. Dynamic Capabilities and Strategic Management. Strateg. Manag. J. 1997, 18, 509–533. [Google Scholar] [CrossRef]
  8. Jraisat, L.; Jreissat, M.; Upadhyay, A.; Kumar, A. Blockchain Technology: The Role of Integrated Reverse Supply Chain Networks in Sustainability. In Proceedings of the Supply Chain Forum: An International Journal; Taylor & Francis: Abingdon, UK, 2023; Volume 24, pp. 17–30. [Google Scholar]
  9. Gomes, G.; Wojahn, R.M. Organizational Learning Capability, Innovation and Performance: Study in Small and Medium-Sized Enterprises (SMES). Rev. Adm. 2017, 52, 163–175. [Google Scholar] [CrossRef]
  10. Baycik, N.O.; Gowda, S. Digitalization of Operations and Supply Chains: Insights from Survey and Case Studies. Digit. Transform. Soc. 2023, 3, 277–295. [Google Scholar] [CrossRef]
  11. Pankowska, M. Information Technology Outsourcing Chain: Literature Review and Implications for Development of Distributed Coordination. Sustainability 2019, 11, 1460. [Google Scholar] [CrossRef]
  12. Ali, A.A.A.; Abualrejal, H.M.E.; Mohamed Udin, Z.B.; Shtawi, H.O.; Alqudah, A.Z. The Role of Supply Chain Integration on Project Management Success in Jordanian Engineering Companies. In Proceedings of the International Conference on Emerging Technologies and Intelligent Systems, Virtual, 21–23 May 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 646–657. [Google Scholar]
  13. Kumar, M.; Raut, R.D.; Mangla, S.K.; Moizer, J.; Lean, J. Big Data Driven Supply Chain Innovative Capability for Sustainable Competitive Advantage in the Food Supply Chain: Resource-based View Perspective. Bus. Strateg. Environ. 2024, 33, 5127–5150. [Google Scholar] [CrossRef]
  14. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications. Eur. J. Tour. Res. 2014, 6, 211–213. [Google Scholar]
  15. Rustina, E.; Tarigan, S.T.; Makbul, Y.; Ie, M.; Pratiwi, H.; Irmawati; Cahyani, N.; Wening, N. The Partnerships and Logistics Leadership in the SMEs: The Impact of Digital Supply Chain Implementation. Uncertain Supply Chain Manag. 2024, 12, 1307–1316. [Google Scholar] [CrossRef]
  16. Elrayah, M.; Keong, O.C. Moderating Effect of Green Technology Adoption on the Relationship of Sustainable Operations Practices and Sustainable Operational Performance. Oper. Res. Eng. Sci. Theory Appl. 2023, 6. Available online: https://oresta.org/menu-script/index.php/oresta/article/view/628 (accessed on 16 June 2025).
  17. de Araujo, L.M.; Priadana, S.; Paramarta, V.; Sunarsi, D. Digital Leadership in Business Organizations. Int. J. Educ. Adm. Manag. Leadersh. 2021, 2, 45–56. [Google Scholar] [CrossRef]
  18. Frankowska, M.; Rzeczycki, A. Reshaping Supply Chain Collaboration-The Role of Digital Leadership in a Networked Organization. In Proceedings of the Boosting Collaborative Networks 4.0: 21st IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2020, Valencia, Spain, 23–25 November 2020; Proceedings 21. Springer: Berlin/Heidelberg, Germany, 2020; pp. 353–364. [Google Scholar]
  19. Asbeetah, Z.; Alzubi, A.; Khadem, A.; Iyiola, K. Harnessing Digital Transformation for Sustainable Performance: Exploring the Mediating Roles of Green Knowledge Acquisition and Innovation Performance Under Digital Transformational Leadership. Sustainability 2025, 17, 2285. [Google Scholar] [CrossRef]
  20. Hayyat, A. The Effect of Organizational Green Operations and Digitalization to Promote Green Supply Chain Performance. In Human Perspectives of Industry 4.0 Organizations; CRC Press: Boca Raton, FL, USA, 2025; pp. 183–223. [Google Scholar]
  21. Liu, X.; Du, W.; Olasehinde, T.; Fan, Y. Balancing Competition and Sustainability: Strategic Supply Chain Configurations in Response to Consumer Low-Carbon Preferences. Sustain. Futur. 2025, 9, 100411. [Google Scholar] [CrossRef]
  22. Pechancová, V.; Hrbáčková, L.; Dvorský, J.; Chromjaková, F.; Stojanović, A. Environmental Management Systems: An Effective Tool of Corporate Sustainability. Entrep. Sustain. Issues 2019, 7, 825–841. [Google Scholar] [CrossRef]
  23. Azwar, W.A.; Syaharuddin, S.; Isnaini, I. Influence of Moral Education, Organizational Involvement, and Social Participation on Environmental Awareness Attitudes. J. Ilmu Pendidik. STKIP Kusuma Negara 2025, 16, 218–231. [Google Scholar]
  24. Karimi, S.K.; Naini, S.G.J.; Sadjadi, S.J. An Integration of Environmental Awareness into Flexible Supply Chains: A Trade-off between Costs and Environmental Pollution. Environ. Sci. Pollut. Res. 2021, 28, 58228–58238. [Google Scholar] [CrossRef]
  25. Javed, A.; Li, Q.; Basit, A. Enhancing Ambidextrous Green Innovation: The Role of Green Supply Chain Integration, Knowledge Capabilities and Technology Dynamism in Manufacturing. J. Manuf. Technol. Manag. 2025, 36, 431–454. [Google Scholar] [CrossRef]
  26. Siddiqi, R.A.; Codini, A.P.; Ishaq, M.I.; Jamali, D.R.; Raza, A. Sustainable Supply Chain, Dynamic Capabilities, Eco-innovation, and Environmental Performance in an Emerging Economy. Bus. Strateg. Environ. 2025, 34, 338–350. [Google Scholar] [CrossRef]
  27. Esan, O.; Ajayi, F.A.; Olawale, O. Managing Global Supply Chain Teams: Human Resource Strategies for Effective Collaboration and Performance. GSC Adv. Res. Rev. 2024, 19, 013–031. [Google Scholar] [CrossRef]
  28. Abrahams, T.O.; Farayola, O.A.; Amoo, O.O.; Ayinla, B.S.; Osasona, F.; Atadoga, A. Continuous Improvement in Information Security: A Review of Lessons from Superannuation Cybersecurity Uplift Programs. Int. J. Sci. Res. Arch. 2024, 11, 1327–1337. [Google Scholar] [CrossRef]
  29. Chowdhury, R.H. Digital Leadership and Organizational Learning: Technologies for Business Transformation and Operational Excellence; Deep Science Publishing: Mumbai, India, 2025; ISBN 9349910039. [Google Scholar]
  30. Tennant, S.; Fernie, S. Organizational Learning in Construction Supply Chains. Eng. Constr. Archit. Manag. 2013, 20, 83–98. [Google Scholar] [CrossRef]
  31. Zada, M.; Khan, S.; Zada, S.; Dhar, B.K. Driving Sustainable Development Through CSR Leadership: Insights Into Organizational Learning and Technological Innovation. Sustain. Dev. 2025, 33, 4060–4074. [Google Scholar] [CrossRef]
  32. Moh’d Anwer, A.-S. Assessing Sustainability of Green Supply Chain Performance: The Roles of Agile Innovative Products, Business Intelligence Readiness, Innovative Supply Chain Process Integration, and Lean Supply Chain Capability as a Mediating Factor. J. Open Innov. Technol. Mark. Complex. 2025, 11, 100476. [Google Scholar]
  33. Alkaraan, F.; Elmarzouky, M.; de Sousa Jabbour, A.B.L.; Jabbour, C.J.C.; Gulko, N. Maximising Sustainable Performance: Integrating Servitisation Innovation into Green Sustainable Supply Chain Management under the Influence of Governance and Industry 4.0. J. Bus. Res. 2025, 186, 115029. [Google Scholar] [CrossRef]
  34. Seman, N.A.A.; Zakuan, N.; Jusoh, A.; Arif, M.S.M.; Saman, M.Z.M. The Relationship of Green Supply Chain Management and Green Innovation Concept. Procedia-Social Behav. Sci. 2012, 57, 453–457. [Google Scholar] [CrossRef]
  35. Hosen, M.I.; Tushar, S.R.; Alam, M.F.B.; Syduzzaman, M. Strategies for Economic Sustainability: An Empirical Study on Muri, Mura, and Muda in the Readymade Garment Sector. Green Technol. Sustain. 2025, 3, 100115. [Google Scholar] [CrossRef]
  36. Junaid, M.; Zhang, Q.; Syed, M.W. Effects of Sustainable Supply Chain Integration on Green Innovation and Firm Performance. Sustain. Prod. Consum. 2022, 30, 145–157. [Google Scholar] [CrossRef]
  37. Do, M.-H.; Huang, Y.-F.; Hoang, T.-T. Blockchain Adoption in Green Supply Chains: Analyzing Key Drivers, Green Innovation, and Expected Benefits. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 39. [Google Scholar] [CrossRef]
  38. Farrukh Shahzad, M.; Liu, H.; Zahid, H. Industry 4.0 Technologies and Sustainable Performance: Do Green Supply Chain Collaboration, Circular Economy Practices, Technological Readiness and Environmental Dynamism Matter? J. Manuf. Technol. Manag. 2025, 36, 1–22. [Google Scholar] [CrossRef]
  39. Singh, J.; Singh, H.; Kumar, A. Impact of Lean Practices on Organizational Sustainability through Green Supply Chain Management—An Empirical Investigation. Int. J. Lean Six Sigma 2020, 11, 1049–1082. [Google Scholar] [CrossRef]
  40. Oubrahim, I.; Sefiani, N. An Integrated Multi-Criteria Decision-Making Approach for Sustainable Supply Chain Performance Evaluation from a Manufacturing Perspective. Int. J. Product. Perform. Manag. 2025, 74, 304–339. [Google Scholar] [CrossRef]
  41. Vern, P.; Panghal, A.; Mor, R.S.; Kumar, V.; Sarwar, D. Unlocking the Potential: Leveraging Blockchain Technology for Agri-Food Supply Chain Performance and Sustainability. Int. J. Logist. Manag. 2025, 36, 474–500. [Google Scholar] [CrossRef]
  42. Waqar, A.; Houda, M.; Ahsan, M.; Nisar, S. Enhancing Project Performance through Sustainable Supply Chain Management: A Comprehensive Analysis of Residential Construction Practices. Environ. Chall. 2025, 18, 101075. [Google Scholar] [CrossRef]
  43. Wang, J.; Duan, K.; Zheng, Y. Green Supply Chain Management, Green Technology Innovation and Firms’ Energy Consumption Intensity. Energy Econ. 2025, 141, 108133. [Google Scholar] [CrossRef]
  44. Alnsour, J.; Arabeyyat, A.R.; Al-Hyari, K.; Al-Bazaiah, S.A.I.; Aldweik, R. Enhancing City Logistics for Sustainable Development in Jordan: A Survey-Based Study. Logistics 2023, 8, 1. [Google Scholar] [CrossRef]
  45. Samawi, G.A.; Bwaliez, O.M.; Dmour, W.A.; Al Mdanat, M.F.; Ta’Amnha, M.A. Eco-Smart Economics: Revolutionizing Jordan’s Logistics with Sustainable Drone Technology. Int. J. Energy Econ. Policy 2024, 14, 49–61. [Google Scholar] [CrossRef]
  46. Teece, D.J.; Pisano, G.; Shuen, A. Dynamic Capabilities and Strategic Management. In The Nature and Dynamics of Organizational Capabilities; Oxford Academic: Oxford, UK, 2001; pp. 334–362. [Google Scholar] [CrossRef]
  47. Govardhan, S.; Narkhede, B.E.; Raut, R.D.; Zhang, L.L.; Ghoshal, S. Significance of Industry 4.0 in Achieving Sustainable Performance Across Supply Chains—A Research Perspective. Bus. Strateg. Dev. 2025, 8, e70064. [Google Scholar] [CrossRef]
  48. Stephens, M. Digital Leadership in a Global Context. In Digital Leadership; Edward Elgar Publishing: Cheltenham, UK, 2025; pp. 9–29. ISBN 1035321246. [Google Scholar]
  49. Sabri, E.H. Mastering Change Management in AI-Powered Supply Chain Digital Transformation. In Supply Chain Transformation Through Generative AI and Machine Learning; IGI Global Scientific Publishing: Hershey, PA, USA, 2025; pp. 153–182. [Google Scholar]
  50. Ali, A.A.A.; Udin, Z.M.; Abualrejal, H.M.E. The Impact of Humanitarian Supply Chain on Non-Government Organizations Performance Moderated by Organisation Culture. Int. J. Sustain. Dev. Plan. 2023, 18, 763–772. [Google Scholar] [CrossRef]
  51. Rahmadani, P. The Role of Digital Leadership in Driving Business Innovation and Competitive Advantage. 2025. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5127850#:~:text=The%20findings%20indicate%20that%20digital,a%20culture%20of%20continuous%20learning (accessed on 20 May 2025).
  52. Mulya, E.A.W.; Kusumawardhani, A. The impact of green marketing on customer purchase intention: The mediating role of customer concerns and beliefs and green brand knowledge. Diponegoro Int. J. Bus. 2023, 6, 90–103. [Google Scholar] [CrossRef]
  53. Orji, I.J.; Kusi-Sarpong, S.; Okwara, U.K. Sustainability and the Digital Supply Chain BT. In The Palgrave Handbook of Supply Chain Management; Sarkis, J., Ed.; Springer International Publishing: Cham, Switzerland, 2024; pp. 1467–1485. ISBN 978-3-031-19884-7. [Google Scholar]
  54. Norrman, A.; Wieland, A. The Development of Supply Chain Risk Management over Time: Revisiting Ericsson. Int. J. Phys. Distrib. Logist. Manag. 2020, 50, 641–666. [Google Scholar] [CrossRef]
  55. García-Morales, V.J.; Jiménez-Barrionuevo, M.M.; Gutiérrez-Gutiérrez, L. Transformational Leadership Influence on Organizational Performance through Organizational Learning and Innovation. J. Bus. Res. 2012, 65, 1040–1050. [Google Scholar] [CrossRef]
  56. Tang, Y.M.; Chau, K.Y.; Lau, Y.Y.; Zheng, Z. Data-Intensive Inventory Forecasting with Artificial Intelligence Models for Cross-Border E-Commerce Service Automation. Appl. Sci. 2023, 13, 3051. [Google Scholar] [CrossRef]
  57. Sarstedt, M.; Ringle, C.M.; Hair, J.F. Partial Least Squares Structural Equation Modeling. In Handbook of Market Research; Springer: Berlin/Heidelberg, Germany, 2021; pp. 587–632. [Google Scholar]
  58. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to Use and How to Report the Results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  59. Shmueli, G.; Sarstedt, M.; Hair, J.F.; Cheah, J.-H.; Ting, H.; Vaithilingam, S.; Ringle, C.M. Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. Eur. J. Mark. 2019, 53, 2322–2347. [Google Scholar] [CrossRef]
  60. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  61. Henseler, J.; Ringle, C.M.; Sarstedt, M. A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
  62. Hair, J.F.; Sarstedt, M.; Ringle, C.M. Rethinking Some of the Rethinking of Partial Least Squares. Eur. J. Mark. 2019, 53, 566–584. [Google Scholar] [CrossRef]
  63. AL-khatib, A.W.; Ramayah, T. Artificial Intelligence-Based Dynamic Capabilities and Circular Supply Chain: Analyzing the Potential Indirect Effect of Frugal Innovation in Retailing Firms. Bus. Strateg. Environ. 2025, 34, 830–848. [Google Scholar] [CrossRef]
  64. Rejeb, A.; Rejeb, K. Blockchain and Supply Chain Sustainability. Logforum 2020, 16, 363–372. [Google Scholar] [CrossRef]
  65. AL-Khatib, A.W. The Determinants of Export Performance in the Digital Transformation Era: Empirical Evidence from Manufacturing Firms. Int. J. Emerg. Mark. 2024, 19, 2597–2622. [Google Scholar] [CrossRef]
  66. Ezmigna, I.; Alghizzawi, M.; Alqsass, M.; Abu-AlSondos, I.A.; Abualfalayeh, G.; Abdeldayem, M.M. The Role of Vision 2030 in Transforming Saudi Arabia’s Logistics Sector: A Qualitative Approach. In Proceedings of the 2024 International Conference on Decision Aid Sciences and Applications (DASA), Manama, Bahrain, 11–12 December 2024; pp. 1–6. [Google Scholar]
  67. Anser, M.K.; Khan, M.A.; Awan, U.; Batool, R.; Zaman, K.; Imran, M.; Sasmoko; Indrianti, Y.; Khan, A.; Bakar, Z.A. The Role of Technological Innovation in a Dynamic Model of the Environmental Supply Chain Curve: Evidence from a Panel of 102 Countries. Processes 2020, 8, 1033. [Google Scholar] [CrossRef]
Sustainability 17 05716 g001
Figure 1. Framework of The Study.
Figure 1. Framework of The Study.
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Figure 2. Result of the Measurement Model.
Figure 2. Result of the Measurement Model.
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Figure 3. Result of Structural Model.
Figure 3. Result of Structural Model.
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Table 1. Factor Loadings.
Table 1. Factor Loadings.
ConstructsItemsFactor LoadingsCronbach’s AlphaComposite Reliability (Rho_a)Composite Reliability (Rho_c)(AVE)
Digital LeadershipDL_10.823 0.863 0.871 0.906 0.707
DL_20.860
DL_30.810
DL_40.869
Environmental AwarenessEA_1 0.767 0.749 0.755 0.841 0.570
EA_2 0.736
EA_3 0.802
EA_4 0.712
Green Supply Chain InnovationGSC_1 0.721 0.878 0.882 0.911 0.674
GSC_2 0.836
GSC_3 0.828
GSC_4 0.849
GSC_5 0.862
Organizational LearningOL_1 0.765 0.783 0.790 0.859 0.604
OL_2 0.767
OL_3 0.790
OL_4 0.788
Sustainable Supply Chain PerformanceSSCP_1 0.828 0.823 0.829 0.876 0.587
SSCP_2 0.759
SSCP_3 0.704
SSCP_4 0.741
SSCP_5 0.793
Table 2. HTMT.
Table 2. HTMT.
Digital LeadershipEnvironmental AwarenessGreen Supply Chain InnovationOrganizational LearningSustainable Supply Chain Performance
Digital Leadership
Environmental Awareness0.670
Green Supply Chain Innovation0.7960.705
Organizational Learning0.5200.6490.710
Sustainable Supply Chain Performance0.7790.6600.6610.623
Table 3. Fornell and Larcker Correlation.
Table 3. Fornell and Larcker Correlation.
Digital LeadershipEnvironmental AwarenessGreen Supply Chain InnovationOrganizational LearningSustainable Supply Chain Performance
Digital Leadership0.841
Environmental Awareness0.5510.755
Green Supply Chain Innovation0.7010.5740.821
Organizational Learning0.4380.5050.6000.777
Sustainable Supply Chain Performance0.6640.5190.7250.5090.766
Table 4. Hypotheses Testing (Path Coefficients—β).
Table 4. Hypotheses Testing (Path Coefficients—β).
HypothesesPath Coefficients—βStandard Deviation T Statistics p Values Decision
H1: Digital Leadership → Green Supply Chain Innovation 0.4810.052 9.229 0.000 Supported
H2: Digital Leadership → Sustainable Supply Chain Performance 0.3970.046 8.709 0.000 Supported
H3: Environmental Awareness → Green Supply Chain Innovation 0.1510.048 3.158 0.002 Supported
H4: Environmental Awareness → Sustainable Supply Chain Performance 0.1240.040 3.114 0.002 Supported
H5: Green Supply Chain Innovation → Sustainable Supply Chain Performance0.8250.019 43.340 0.000 Strongly Supported
H6: Organizational Learning → Green Supply Chain Innovation0.3130.052 5.994 0.000 Supported
H7: Organizational Learning → Sustainable Supply Chain Performance0.2580.043 6.079 0.000 Supported
Table 5. R2 and Model Fit.
Table 5. R2 and Model Fit.
Endogenous ConstructR-SquareR-Square Adjusted
Green Supply Chain Innovation0.6110.608
Sustainable Supply Chain Performance0.6800.679
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Atieh, A.A.; Abushaega, M.M. Achieving Supply Chain Sustainability Through Green Innovation: A Dynamic Capabilities-Based Approach in the Logistics Sector. Sustainability 2025, 17, 5716. https://doi.org/10.3390/su17135716

AMA Style

Atieh AA, Abushaega MM. Achieving Supply Chain Sustainability Through Green Innovation: A Dynamic Capabilities-Based Approach in the Logistics Sector. Sustainability. 2025; 17(13):5716. https://doi.org/10.3390/su17135716

Chicago/Turabian Style

Atieh, Ahmad Ali, and Mastoor M. Abushaega. 2025. "Achieving Supply Chain Sustainability Through Green Innovation: A Dynamic Capabilities-Based Approach in the Logistics Sector" Sustainability 17, no. 13: 5716. https://doi.org/10.3390/su17135716

APA Style

Atieh, A. A., & Abushaega, M. M. (2025). Achieving Supply Chain Sustainability Through Green Innovation: A Dynamic Capabilities-Based Approach in the Logistics Sector. Sustainability, 17(13), 5716. https://doi.org/10.3390/su17135716

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